Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into fix_maxpool_with_mask_layer

benchmark/cluster/vgg16/vgg16_fluid.py

@@ -240,8 +240,6 @@ def main():
 
         t = fluid.DistributeTranspiler()
         t.transpile(
-            optimize_ops,
-            params_grads,
             trainer_id=args.task_index,
             pservers=args.ps_hosts,
             trainers=trainers)

doc/v2/howto/cluster/multi_cluster/index_en.rst

@@ -8,28 +8,28 @@ The user's cluster environment is not the same. To facilitate everyone's deploym
 ..  toctree::
   :maxdepth: 1
 
-  k8s_cn.md
-  k8s_distributed_cn.md
+  k8s_en.md
+  k8s_distributed_en.md
 
 `OpenMPI <https://www.open-mpi.org>`_ is a mature high-performance parallel computing framework, which is widely used in the field of HPC. The following guide describes how to use OpenMPI to build PaddlePaddle's cluster training task:
 
 ..  toctree::
   :maxdepth: 1
 
-  openmpi_cn.md
+  openmpi_en.md
 
 `Fabric <http://www.fabfile.org>`_ is a convenient tool for program deployment and management. We provide a way to deploy and manage with Fabric. If you want to know more about it, please read the following guidelines:
 
 ..  toctree::
   :maxdepth: 1
 
-  fabric_cn.md
+  fabric_en.md
 
 We also support the deployment of PaddlePaddle on AWS. Learn more about:
 
 ..  toctree::
   :maxdepth: 1
 
-  k8s_aws_cn.md
+  k8s_aws_en.md
 
-The examples can be found under `cluster_train_v2 <https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/scripts/cluster_train_v2>`_ .
+The examples can be found under `cluster_train_v2 <https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/scripts/cluster_train_v2>`_ .

python/paddle/fluid/__init__.py

@@ -21,14 +21,15 @@ import executor
 from executor import *
 
 import trainer
-from trainer import *
+from trainer import Trainer
+from trainer import BeginEpochEvent
+from trainer import EndEpochEvent
+from trainer import BeginStepEvent
+from trainer import EndStepEvent
 
 import inferencer
 from inferencer import Inferencer
 
-import params
-from params import Params
-
 import io
 import evaluator
 import initializer
@@ -57,7 +58,7 @@ from parallel_executor import ParallelExecutor
 Tensor = LoDTensor
 
 __all__ = framework.__all__ + executor.__all__ + concurrency.__all__ +\
-          trainer.__all__ + inferencer.__all__ + params.__all__ + [
+          trainer.__all__ + inferencer.__all__ + [
     'io',
     'initializer',
     'layers',

python/paddle/fluid/distribute_transpiler.py

@@ -137,8 +137,6 @@ def split_dense_variable(var_list,
 
 class DistributeTranspiler:
     def transpile(self,
-                  optimize_ops,
-                  params_grads,
                   trainer_id,
                   program=None,
                   pservers="127.0.0.1:6174",
@@ -169,11 +167,6 @@ class DistributeTranspiler:
             4. append ops that should run on current server instance.
             5. add listen_and_serv op
 
-            :param optimize_ops: op list of optimization, should be the
-                                    return value of Optimizer.minimize
-            :type optimize_ops: list
-            :param params_grads: list of tuple(weight, gradient)
-            :type params_grads: list
             :param trainer_id: one unique id for each trainer in a job.
             :type trainer_id: int
             :param program: program to transpile, default is default_main_program
@@ -194,7 +187,6 @@ class DistributeTranspiler:
             program = default_main_program()
         self.origin_program = program
         self.trainer_num = trainers
-        self.optimize_ops = optimize_ops
         self.sync_mode = sync_mode
         # TODO(typhoonzero): currently trainer_id is fetched from cluster system
         # like Kubernetes, we should port this to use etcd later when developing
@@ -202,6 +194,7 @@ class DistributeTranspiler:
         self.trainer_id = trainer_id
         pserver_endpoints = pservers.split(",")
         self.pserver_endpoints = pserver_endpoints
+        self.optimize_ops, params_grads = self._get_optimize_pass()
 
         # process lookup_table_op
         # 1. check all lookup_table_op is distributed
@@ -408,11 +401,8 @@ class DistributeTranspiler:
         # HACK: optimization global ops only used to scale beta1 and beta2
         # replace it with dependency engine.
         for op in self.optimize_ops:
-            if op.type == "scale":
-                for in_name in op.input_arg_names:
-                    if in_name.startswith("beta1_pow_acc") or \
-                            in_name.startswith("beta2_pow_acc"):
-                        global_ops.append(op)
+            if self._is_adam_connected_op(op):
+                global_ops.append(op)
 
         def __append_optimize_op__(op, block, grad_to_block_id):
             if self._is_opt_op(op):
@@ -1147,3 +1137,32 @@ class DistributeTranspiler:
                     # we only need to append op for once
                     break
         return lr_ops
+
+    def _get_optimize_pass(self):
+        block = self.origin_program.global_block()
+        opt_ops = []
+        params_grads = []
+        for op in block.ops:
+            if self._is_opt_op(op):
+                opt_ops.append(op)
+                params_grads.append((self.origin_program.global_block().var(
+                    op.input("Param")[0]),
+                                     self.origin_program.global_block().var(
+                                         op.input("Grad")[0])))
+            elif self._is_adam_connected_op(op):
+                opt_ops.append(op)
+            else:
+                pass
+        return opt_ops, params_grads
+
+    def _is_adam_connected_op(self, op):
+        """
+        A hack function to determinate whether the input operator
+        is connected to optimize operator.
+        """
+        if op.type == "scale":
+            for in_name in op.input_arg_names:
+                if in_name.startswith("beta1_pow_acc") or \
+                        in_name.startswith("beta2_pow_acc"):
+                    return True
+        return False

python/paddle/fluid/inferencer.py

@@ -12,18 +12,22 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import core
+
 __all__ = ['Inferencer', ]
 
 
 class Inferencer(object):
-    def __init__(self, network_func, params, place=None):
+    def __init__(self, network_func, param_path=None, place=None):
         # 1. we need to generate a framework.Program by calling
         # network_func. Reference: fluid.program_guard in test_word2vec.py
 
         # 2. move the default_main_program to self.program.
 
         # 3. run the default_startup program.
-        self.params = params
+
+        # 4. load params from param_path into scope
+        self.scope = core.Scope()
         self.place = place
 
     def infer(self, inputs):

python/paddle/fluid/params.py

@@ -1,39 +0,0 @@
-#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from . import core
-
-__all__ = ['Params', ]
-
-
-class Params(object):
-    def __init__(self, path=None):
-        self.scope = core.Scope()
-
-        if path:
-            self._load(path)
-
-    def _load(self, path):
-        # reference: load_persistables in io.py
-        pass
-
-    def save(self, path):
-        # reference: save_persistables in io.py
-        pass
-
-    def add_params(self, scope):
-        # take the keys from the scope,
-        # if not already exists in self.scope,
-        # add the key and value into self.scope.
-        pass

python/paddle/fluid/tests/book/test_fit_a_line.py

@@ -80,12 +80,7 @@ def train(use_cuda, save_dirname, is_local):
         trainer_id = int(os.getenv("PADDLE_INIT_TRAINER_ID"))
         training_role = os.getenv("TRAINING_ROLE", "TRAINER")
         t = fluid.DistributeTranspiler()
-        t.transpile(
-            optimize_ops,
-            params_grads,
-            trainer_id,
-            pservers=pserver_endpoints,
-            trainers=trainers)
+        t.transpile(trainer_id, pservers=pserver_endpoints, trainers=trainers)
         if training_role == "PSERVER":
             pserver_prog = t.get_pserver_program(current_endpoint)
             pserver_startup = t.get_startup_program(current_endpoint,

python/paddle/fluid/tests/book/test_image_classification.py

@@ -189,12 +189,7 @@ def train(net_type, use_cuda, save_dirname, is_local):
         trainer_id = int(os.getenv("PADDLE_INIT_TRAINER_ID"))
         training_role = os.getenv("TRAINING_ROLE", "TRAINER")
         t = fluid.DistributeTranspiler()
-        t.transpile(
-            optimize_ops,
-            params_grads,
-            trainer_id,
-            pservers=pserver_endpoints,
-            trainers=trainers)
+        t.transpile(trainer_id, pservers=pserver_endpoints, trainers=trainers)
         if training_role == "PSERVER":
             pserver_prog = t.get_pserver_program(current_endpoint)
             pserver_startup = t.get_startup_program(current_endpoint,

python/paddle/fluid/tests/book/test_label_semantic_roles.py

@@ -259,12 +259,7 @@ def train(use_cuda, save_dirname=None, is_local=True):
         trainer_id = int(os.getenv("PADDLE_INIT_TRAINER_ID"))
         training_role = os.getenv("TRAINING_ROLE", "TRAINER")
         t = fluid.DistributeTranspiler()
-        t.transpile(
-            optimize_ops,
-            params_grads,
-            trainer_id,
-            pservers=pserver_endpoints,
-            trainers=trainers)
+        t.transpile(trainer_id, pservers=pserver_endpoints, trainers=trainers)
         if training_role == "PSERVER":
             pserver_prog = t.get_pserver_program(current_endpoint)
             pserver_startup = t.get_startup_program(current_endpoint,

python/paddle/fluid/tests/book/test_machine_translation.py

@@ -231,12 +231,7 @@ def train_main(use_cuda, is_sparse, is_local=True):
         trainer_id = int(os.getenv("PADDLE_INIT_TRAINER_ID"))
         training_role = os.getenv("TRAINING_ROLE", "TRAINER")
         t = fluid.DistributeTranspiler()
-        t.transpile(
-            optimize_ops,
-            params_grads,
-            trainer_id,
-            pservers=pserver_endpoints,
-            trainers=trainers)
+        t.transpile(trainer_id, pservers=pserver_endpoints, trainers=trainers)
         if training_role == "PSERVER":
             pserver_prog = t.get_pserver_program(current_endpoint)
             pserver_startup = t.get_startup_program(current_endpoint,

python/paddle/fluid/tests/book/test_recognize_digits.py

@@ -162,12 +162,7 @@ def train(nn_type,
         trainer_id = int(os.getenv("PADDLE_INIT_TRAINER_ID"))
         training_role = os.getenv("TRAINING_ROLE", "TRAINER")
         t = fluid.DistributeTranspiler()
-        t.transpile(
-            optimize_ops,
-            params_grads,
-            trainer_id,
-            pservers=pserver_endpoints,
-            trainers=trainers)
+        t.transpile(trainer_id, pservers=pserver_endpoints, trainers=trainers)
         if training_role == "PSERVER":
             pserver_prog = t.get_pserver_program(current_endpoint)
             pserver_startup = t.get_startup_program(current_endpoint,

python/paddle/fluid/tests/book/test_recommender_system.py

@@ -261,12 +261,7 @@ def train(use_cuda, save_dirname, is_local=True):
         trainer_id = int(os.getenv("PADDLE_INIT_TRAINER_ID"))
         training_role = os.getenv("TRAINING_ROLE", "TRAINER")
         t = fluid.DistributeTranspiler()
-        t.transpile(
-            optimize_ops,
-            params_grads,
-            trainer_id,
-            pservers=pserver_endpoints,
-            trainers=trainers)
+        t.transpile(trainer_id, pservers=pserver_endpoints, trainers=trainers)
         if training_role == "PSERVER":
             pserver_prog = t.get_pserver_program(current_endpoint)
             pserver_startup = t.get_startup_program(current_endpoint,

python/paddle/fluid/tests/book/test_understand_sentiment.py

@@ -213,12 +213,7 @@ def train(word_dict,
         trainer_id = int(os.getenv("PADDLE_INIT_TRAINER_ID"))
         training_role = os.getenv("TRAINING_ROLE", "TRAINER")
         t = fluid.DistributeTranspiler()
-        t.transpile(
-            optimize_ops,
-            params_grads,
-            trainer_id,
-            pservers=pserver_endpoints,
-            trainers=trainers)
+        t.transpile(trainer_id, pservers=pserver_endpoints, trainers=trainers)
         if training_role == "PSERVER":
             pserver_prog = t.get_pserver_program(current_endpoint)
             pserver_startup = t.get_startup_program(current_endpoint,

python/paddle/fluid/tests/book/test_word2vec.py

@@ -145,12 +145,7 @@ def train(use_cuda, is_sparse, is_parallel, save_dirname, is_local=True):
         trainer_id = int(os.getenv("PADDLE_INIT_TRAINER_ID"))
         training_role = os.getenv("TRAINING_ROLE", "TRAINER")
         t = fluid.DistributeTranspiler()
-        t.transpile(
-            optimize_ops,
-            params_grads,
-            trainer_id,
-            pservers=pserver_endpoints,
-            trainers=trainers)
+        t.transpile(trainer_id, pservers=pserver_endpoints, trainers=trainers)
         if training_role == "PSERVER":
             pserver_prog = t.get_pserver_program(current_endpoint)
             pserver_startup = t.get_startup_program(current_endpoint,

python/paddle/fluid/tests/book/word2vec/no_test_word2vec_new_api.py

@@ -39,7 +39,7 @@ word_dict = paddle.dataset.imikolov.build_dict()
 dict_size = len(word_dict)
 
 
-def inference_network(is_sparse):
+def inference_program(is_sparse):
     first_word = fluid.layers.data(name='firstw', shape=[1], dtype='int64')
     second_word = fluid.layers.data(name='secondw', shape=[1], dtype='int64')
     third_word = fluid.layers.data(name='thirdw', shape=[1], dtype='int64')
@@ -79,9 +79,9 @@ def inference_network(is_sparse):
     return predict_word
 
 
-def train_network(is_sparse):
+def train_program(is_sparse):
     next_word = fluid.layers.data(name='nextw', shape=[1], dtype='int64')
-    predict_word = inference_network(is_sparse)
+    predict_word = inference_program(is_sparse)
     cost = fluid.layers.cross_entropy(input=predict_word, label=next_word)
     avg_cost = fluid.layers.mean(cost)
     return avg_cost
@@ -100,23 +100,25 @@ def train(use_cuda, is_sparse, save_path):
                 word_dict, N))
 
             if avg_cost < 5.0:
-                trainer.params.save(save_path)
+                trainer.save_params(save_path)
                 return
             if math.isnan(avg_cost):
                 sys.exit("got NaN loss, training failed.")
 
     trainer = fluid.Trainer(
-        partial(train_network, is_sparse),
+        partial(train_program, is_sparse),
         fluid.optimizer.SGD(learning_rate=0.001),
         place=place)
     trainer.train(
         reader=train_reader, num_epochs=100, event_handler=event_handler)
 
 
-def infer(use_cuda, save_path):
-    params = fluid.Params(save_path)
+def infer(use_cuda, is_sparse, save_path):
     place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
-    inferencer = fluid.Inferencer(inference_network, params, place=place)
+    inferencer = fluid.Inferencer(
+        partial(inference_program, is_sparse),
+        param_path=save_path,
+        place=place)
 
     lod = [0, 1]
     first_word = create_random_lodtensor(lod, place, low=0, high=dict_size - 1)
@@ -138,7 +140,7 @@ def main(use_cuda, is_sparse):
 
     save_path = "word2vec.inference.model"
     train(use_cuda, is_sparse, save_path)
-    infer(use_cuda, save_path)
+    infer(use_cuda, is_sparse, save_path)
 
 
 if __name__ == '__main__':

python/paddle/fluid/trainer.py

@@ -56,23 +56,22 @@ class Trainer(object):
     """
 
     Args:
-        network_func(callable): A function which will return loss. The loss must be a scaler.
+        program_func(callable): A function which will return loss. The loss must be a scaler.
         optimizer(optimizer.Optimizer): The optimizer should be an instance of Optimizer
-        params:
         place: The device place of this trainer.
     """
 
-    def __init__(self, network_func, optimizer, params=None, place=None):
+    def __init__(self, program_func, optimizer, param_path=None, place=None):
         # 1. we need to generate a framework.Program by calling
-        # network_func. Reference: fluid.program_guard in
+        # program_func. Reference: fluid.program_guard in
         # test_word2vec.py
-        self.scope = self._get_scope_from_params(params)
+        self.scope = core.Scope()
 
         self.startup_program = framework.Program()
         self.train_program = framework.Program()
 
         with framework.program_guard(self.train_program, self.startup_program):
-            loss = network_func()
+            loss = program_func()
             if not isinstance(optimizer, opt_module.Optimizer):
                 raise TypeError(
                     "The optimizer should be an instance of Optimizer")
@@ -84,14 +83,13 @@ class Trainer(object):
         # 2. move the default_main_program to self.program and run the
         # default_startup program on an empty core.Scope()
         # Run startup program
-        if params is None:
-            exe = executor.Executor(place)
-            exe.run(self.startup_program, scope=self.scope)
+        exe = executor.Executor(place)
+        exe.run(self.startup_program, scope=self.scope)
 
-        # 3. call self.params.add_vars with the initialized scope, it
-        # will add the new vars of the initialized scope into
-        # self.params.
-        # TODO(yuyang): This depends on parameters implementation.
+        if param_path:
+            # load params from param_path into scope
+            # TODO(yuyang): This depends on parameters implementation.
+            pass
 
         # TODO(helin): support distributed training
 
@@ -124,19 +122,9 @@ class Trainer(object):
     def test(self, reader):
         pass
 
-    def _get_scope_from_params(self, params):
-        """
-        Get Scope from parameter object.
-        Args:
-            params(Parameter|None): The parameter object instance. Could be None.
-
-        Returns: New scope if params is None. Or params.scope()
-        NOTE: This method is WIP. Not fully implemented.
-        """
-        if params is None:
-            return core.Scope()  # new scope when params is None
-        else:
-            raise NotImplementedError("Not implemented right now.")
+    def save_params(self, param_path):
+        # reference: save_persistables in io.py
+        pass
 
     @staticmethod
     def _check_and_get_place(place):